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How often have you hired the wrong person or had employees quit shortly after starting? Probably more than once. Emotions, gut feelings, and biases often influence hiring decisions, hindering inclusion and diversity goals.

Using data for hiring can mitigate these issues.

How? Delve into practical cases in this blog post!

What Is Data-Driven Recruitment?

According to a LinkedIn poll, 77%  of talent professionals rely on analytics to plan their workforce.

Data-driven recruiting uses data and analytics to plan, execute, and improve the recruiting process. Data from various sources is collected and used to make informed hiring decisions.

There are different levels of HR analytics:

  • Operational HR metrics include turnover or vacancy closing rates. You can calculate operational metrics using online tools (for example, calculate the main recruitment KPIs here).
  • Specialized statistical metrics are used to work on more complex tasks and hypotheses. To calculate them, you need to know the basics of statistics, mathematical analysis, and special programming languages (for example, Python or R).

Examples of Data-Driven Recruitment

Here are examples of data analysis at various stages of the recruitment process:


  • Using applicant tracking systems (ATS) to collect and analyze data on candidate sources, such as job boards, social media platforms, and employee referrals. This data helps identify the most effective channels for sourcing high-quality candidates.
  • Implementing predictive analytics to assess the likelihood of candidate success based on past hiring patterns and performance data, allowing recruiters to prioritize candidates with the highest potential.

Candidate Screening

  • Automated resume screening tools that use machine learning algorithms to match candidate qualifications with job requirements, reducing manual effort and bias in the initial screening process.
  • Analyzing assessment data from pre-employment tests or simulations to identify candidates who possess the skills and competencies necessary for success in the role.

Interviewing and Selection

  • Using video interviewing platforms with built-in analytics to evaluate candidates' verbal and non-verbal cues, communication skills, and cultural fit, providing objective data to inform hiring decisions.
  • Implementing structured interview techniques and scoring rubrics to standardize the evaluation process and collect quantifiable data on candidate responses and performance.

Onboarding and Retention

  • Tracking employee engagement and satisfaction data through surveys and feedback mechanisms to identify areas for improvement in the onboarding process and mitigate early turnover.
  • Analyzing performance metrics and retention rates of hires made through different recruitment strategies or sources to optimize future hiring decisions and enhance long-term retention. Usually, modern ATSs offer built-in analytics for optimizing performance tracking.

Why Is Data-Driven Recruiting So Important?

Figures help to make more balanced management decisions and generally improve the hiring workflow. Let’s take a look at the particular benefits of data driven hiring:

The “After” column sounds like a recruiter’s dream, doesn’t it? Benefits of data driven recruitment are obvious so many companies already use this data driven approach to hiring and other day-to-day HR processes..

Companies Succeded in Data-Based Talent Acquisition

Statistics also empower companies, their HR professionals, and hiring managers to make informed decisions. They offer valuable insights into trends and patterns, enabling organizations to identify areas of strength and areas that require improvement. Let’s take a look at the most successful cases of cool initiatives for data driven recruitment:

Best Practices for Data-Driven Recruitment

Examine past hiring efforts

Looking back at past hiring efforts can give you useful clues for your future data driven approach to recruiting. You can find out what worked well before and do it again.

By looking at past successes, you can see what made them successful. Maybe you found great candidates through certain websites or by using specific tests. You can use these methods again because you know they work.

Also, by looking back, you can learn from your mistakes. If something didn't work before, you can try to fix it or avoid it next time.

In short, by learning from the past, you can make your future hiring efforts better and smarter.

Choose relevant metrics

Prioritize the appropriate metrics and pinpoint reliable data sources. To prevent overwhelm, identify the metrics that enhance your data driven recruitment process and align with your broader business objectives.

For example, there are key metrics most HR and recruitment professionals use, including:

  • time-to-hire (the amount of time it takes from posting a job ad until an offer has been accepted),
  • quality-of-hire (the rate at which new hires become successful employees), and
  • cost-per-hire (the total cost associated with filling a vacancy).

Tracking these metrics enables you to monitor the long-term effectiveness of each recruiting strategy, facilitating adjustments as necessary. For instance, if analysis shows that a particular recruitment source consistently brings in high-quality employees, then investing more in that source would make sense.

Check out our blog post - 15 Best Metrics And KPIs For HR!

Define benchmarks

Calculating metrics makes sense only if you understand the healthy rates of these metrics - a specific benchmark.

Benchmarking your recruitment performance against other businesses can be a valuable tool in identifying areas for improvement. For instance, if a company discovers that its recruiters need to present 10 candidates to managers before making an offer, whereas the industry average is only 5, it suggests a potential bottleneck in the hiring process.

Such a disparity could indicate several underlying issues. For example, it may highlight inefficiencies in the initial screening process, where recruiters are not effectively identifying qualified candidates. Alternatively, it could signal communication gaps between recruiters and hiring managers regarding the desired qualifications and criteria for the role.

By recognizing this "weak link," the company can take proactive steps to address the root cause of the discrepancy. This might involve refining the candidate selection criteria, providing additional training or resources to recruiters, or enhancing collaboration and communication between recruiters and hiring managers.

Use recruitment dashboards

Simply having data won't suffice if you lack the know-how to utilize it effectively. That's why it's crucial to construct a recruitment dashboard. This tool visualizes data, revealing trends and potential issues in your hiring pipeline before they escalate.

A recruitment dashboard helps categorize collected data by hires, budgets, campaigns, and applicants. Focus solely on KPIs that drive hiring success.

You can create data-driven recruitment dashboards using three main methods:

Example of recruitment dashboard in an ATS

Take data-driven actions

Data-driven recruitment isn't effective without taking action based on the collected data. Therefore, after gathering data, it's essential to decide how to utilize it.

Don’t be afraid of taking small actions. Even a minor change in the interviewing process can reduce the time-to-hire or other essential metrics.

For example, Google analyzed tens of thousands of interviews with candidates and concluded that some stages of the interview (especially puzzles) are useless and do not help predict the candidate's success. This helped improve the efficiency of the talent acquisition system.

Recognize the limitations of your data

While data is valuable, it isn't flawless. Your recruitment data may have several limitations:

  • Inability to explain some happenings: Combining various data types provides detailed insights, but interpreting these insights is necessary to understand the nuances of your data driven hiring process.
  • Inability to solve problems: Data-driven recruitment metrics highlight areas of strength and improvement, but the actions taken based on this data depend entirely on your team's discretion.
  • Potential bias: Data prepared by team members might not always be objective. For instance, software grading a recruiter’s performance is likely more reliable than human grading.

Data-Driven Recruitment: Case Study

Recently, Axterior featured a podcast episode of Hiring Talks, where we hosted a conversation with the Talent Operations Specialist from MacPaw, a prominent tech company.

Data-driven recruitment in this company reached enormous heights! MacPaw has notably established a TalentOps department as a distinct function within the organization to bolster the efforts of its Talent Acquisition team in reaching its hiring objectives.

This entails strategizing the required number of hires, leveraging analytics to enhance recruitment practices, optimizing the hiring journey, and ensuring a positive candidate experience.

The Talent Operations department at MacPaw oversees a range of responsibilities, including managing systems and processes related to recruitment, talent management, training and development, performance appraisal, employee engagement, and occasionally payroll and benefits administration. Their focus extends beyond the recruitment phase, aiming to streamline and enhance processes across the entire employee lifecycle.

Our guest shared a case from her professional background where analytics helped to optimize the data-driven recruitment process:


Company X faced the challenge of hiring Software Engineers quickly in a competitive job market. However, their recruitment process, which included a lengthy test task, was hindering their progress. To address this, they turned to data analysis for solutions.

Identifying the issue:

Analyzing the past year's recruitment data revealed the number of candidates reaching the test task stage was comparable to those progressing to the tech interview. This indicated potential inefficiencies in their assessment methods. Moreover, the extended duration of the test task stage, averaging over two weeks, was leading to candidate dropouts.

Using data to drive change:

The hiring team decided to eliminate the test task and introduce tougher tech interviews with live coding assessments. This decision aimed to streamline the data driven hiring and make it more competitive.

Results and Impact:

Following the implementation of these changes, Company X observed significant improvements in their data driven recruitment process. Eliminating the test task led to faster hiring, while the revamped interviews helped identify suitable candidates more effectively. Feedback from candidates and the hiring team confirmed that the changes maintained the quality of hires.


No matter which tool you opt for your data driven recruitment strategies, it's crucial to have a solid grasp of your recruitment team's goals and capabilities.

Start by honestly assessing what you aim to achieve, exploring different tools and possibilities for recruitment analytics, and then making a well-informed choice about the solution that best meets your company's needs. This embodies the essence of data-driven recruitment!

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